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An efficient algorithm for the sparse mixed resultant [chapter]

John Canny, Ioannis Emiris
1993 Lecture Notes in Computer Science  
We propose a compact formula for the mixed resultant of a system of n+1 sparse Laurent polynomials in n variables.  ...  Our algorithm is the rst to present a determinantal formula for arbitrary systems; moreover, its complexity for unmixed systems is polynomial in the resultant degree.  ...  Acknowledgment We wish to thank the anonymous referee for his comments and Ashu Rege for several discussions.  ... 
doi:10.1007/3-540-56686-4_36 fatcat:xz3anlfrpnh73enbbvr6utssw4

Blind Image Seperation Using Forward Difference Method (FDM)

M Jyothirmayi
2011 Signal & Image Processing An International Journal  
The block having the most sparseness is considered to determine the separation matrix. The efficiency of the proposed method is compared with other sparse representation functions.  ...  In the proposed method, the image mixture is first transformed to sparse images. These images are divided into blocks and for each block the sparseness measure 0 norm is applied.  ...  Table 1 shows the Results for all the three methods.  ... 
doi:10.5121/sipij.2011.2410 fatcat:6lkbbuhg3zeu5ok2b2tqfdgfey

Page 6239 of Mathematical Reviews Vol. , Issue 96j [page]

1996 Mathematical Reviews  
(F-INRIA2-SA; Sophia Antipolis) ; Canny, John F. (1-CA-CD; Berkeley, CA) Efficient incremental algorithms for the sparse resultant and the mixed volume. (English summary) J.  ...  In addition, we propose an efficient algorithm for computing the mixed volume of n polynomials in n vari- ables. This computation provides an upper bound on the number of common isolated roots.  ... 

Survivable multicast routing in mixed-graph sparse-splitting optical networks

Costas K. Constantinou, Georgios Ellinas
2013 2013 5th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)  
This paper investigates the survivability of multicast requests in mixed-graph optical networks, where only a fraction of the nodes have optical splitting capabilities (sparse-splitting optical networks  ...  Mixed-graph networks have both bidirectional and unidirectional connections between their nodes, and they result due to resource holding of the already established requests.  ...  Mixed-graph Sparse-splitting Heuristic (MSH)A new multicast routing algorithm for mixed-graph sparse-splitting networks, called Mixed-graph Sparse-splitting Heuristic (MSH), is proposed in this section  ... 
doi:10.1109/icumt.2013.6798406 dblp:conf/icumt/ConstantinouE13 fatcat:jyzec46fcvfynlj3lfa4ryxtca

A compressed sensing approach for underdetermined blind audio source separation with sparse representation

Tao Xu, Wenwu Wang
2009 2009 IEEE/SP 15th Workshop on Statistical Signal Processing  
In this paper, we develop a novel algorithm for this problem based on compressed sensing which is an emerging technique for efficient data reconstruction.  ...  The unknown mixing matrix is firstly estimated from the audio mixtures in the transform domain, as in many existing methods, by a Kmeans clustering algorithm.  ...  The CS, which has attracted growing interests in signal processing, is an efficient technique for data acquisition and reconstruction [2] .  ... 
doi:10.1109/ssp.2009.5278532 fatcat:6qdn73ca6rcedktyr46bq7vgry

Morphological diversity and source separation

J. Bobin, Y. Moudden, J.-L. Starck, M. Elad
2006 IEEE Signal Processing Letters  
MCA has been shown to be an efficient technique in such problems as separating an image into texture and piecewise smooth parts or for inpainting applications.  ...  The algorithm, coined MMCA (Multichannel Morphological Component Analysis), is an extension of the Morphological Component Analysis method (MCA).  ...  The next section will illustrate the efficiency of the MMCA algorithm when the sources to be separated have different morphologies. III. RESULTS A.  ... 
doi:10.1109/lsp.2006.873141 fatcat:bzawmcmfabgxvdeobxpeenwy4i

Separation of reflections via sparse ICA

Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky, Yehoshua Y. Zeevi, Michael A. Unser, Akram Aldroubi, Andrew F. Laine
2003 Wavelets: Applications in Signal and Image Processing X  
Simulations and experimental results illustrate the efficiency of the proposed approach, and of its specific implementation in a simple algorithm of a low computational cost.  ...  We extend the Sparse ICA (SPICA) approach and apply it to the separation of the desired image from the superimposed images, without having any a priory knowledge about its structure and/or statistics.  ...  We thank Hani Farid for his images and mixtures data, for his codes and for his comments. This  ... 
doi:10.1117/12.505210 fatcat:u33jrhvpmjelniufmykialikt4

Bayesian Orthogonal Component Analysis for Sparse Representation

Nicolas Dobigeon, Jean-Yves Tourneret
2010 IEEE Transactions on Signal Processing  
A non-informative prior distribution defined on an appropriate Stiefel manifold is elected for the mixing matrix.  ...  This under-complete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with an unknown orthogonal mixing matrix.  ...  Chabert (University of Toulouse) for their valuable feedback regarding the application of BOCA on natural images considered in this work.  ... 
doi:10.1109/tsp.2010.2041594 fatcat:u6flwznivnctfcy56ft3jhcjty

Robust Recovery of Signals From a Structured Union of Subspaces

Yonina C. Eldar, Moshe Mishali
2009 IEEE Transactions on Information Theory  
We then propose a mixed'2='1 program for block sparse recovery. Our main result is an equivalence condition under which the proposed convex algorithm is guaranteed to recover the original signal.  ...  To derive an efficient and robust recovery algorithm, we show that our problem can be formulated as that of recovering a block-sparse vector whose nonzero elements appear in fixed blocks.  ...  ACKNOWLEDGMENT The authors would like to thank Volken Cevher for fruitful discussions regarding the MCS framework.  ... 
doi:10.1109/tit.2009.2030471 fatcat:soq2lbx435exveayn4upozp3dq

Deep Adaptive Network: An Efficient Deep Neural Network with Sparse Binary Connections [article]

Xichuan Zhou, Shengli Li, Kai Qin, Kunping Li, Fang Tang, Shengdong Hu, Shujun Liu, Zhi Lin
2016 arXiv   pre-print
Furthermore, for efficient hardware implementations, the sparse-and-binary-weighted deep neural network could save about 99.3% memory and 99.9% computation units without significant loss of classification  ...  To address this challenge, this paper presents a hardware-oriented deep learning algorithm, named as the Deep Adaptive Network, which attempts to exploit the sparsity in the neural connections.  ...  Adaptive RBM with Mixed Norm Regularization For efficient embedded implementations, we propose a sparsely weighted variant of the RBM, named as the Adaptive RBM (AdaRBM), which adds an extra regularization  ... 
arXiv:1604.06154v1 fatcat:wqak66fwmfc6dk43rbsib2mp4i

Underdetermined Blind Source Separation for Heart Sound Using Higher-order Statistics and Sparse Representation

Yuan Xie, Kan Xie, Shengli Xie
2019 IEEE Access  
Furthermore, an improved l 1 -norm minimization algorithm is proposed to estimate the source signals.  ...  Then, the estimation of the mixing matrix is processed using a higher-order cumulant-based method so that the uniqueness of the estimated mixing matrix is guaranteed.  ...  A fast and efficient algorithm has been proposed to learn an overcomplete dictionary for the sparse representation of signals [34] . Meanwhile, to obtain better sparse solutions, Li et al.  ... 
doi:10.1109/access.2019.2925896 fatcat:biqjokn63ratnkgu4nm6hz2unu

Data structures in Java for matrix computations

Geir Gundersen, Trond Steihaug
2004 Concurrency and Computation  
This data structure is unique for Java and shown to be more dynamic and efficient than the traditional storage schemes for large sparse matrices.  ...  We show how to create efficient dynamic data structures for sparse matrix computations using Java's native arrays.  ...  The coordinate storage format is not an efficient storage format for large sparse compared with compressed row format [15] .  ... 
doi:10.1002/cpe.793 fatcat:4rlz7bdl2neuhixlox3ov52a3q

Survey of sparse and non-sparse methods in source separation

Paul D. O'Grady, Barak A. Pearlmutter, Scott T. Rickard
2005 International journal of imaging systems and technology (Print)  
When the information about the mixing process and sources is limited, the problem is called 'blind'.  ...  The separation of a superposition of multiple signals is accomplished by taking into account the structure of the mixing process and by making assumptions about the sources.  ...  Acknowledgements Supported by Higher Education Authority of Ireland (An tÚdarás Um Ard-Oideachas) and Science Foundation Ireland grant 00/PI.1/C067.  ... 
doi:10.1002/ima.20035 fatcat:bmwu7qm6e5dshfp4tihztwwuiq

A block-based compressed sensing method for underdetermined blind speech separation incorporating binary mask

Tao Xu, Wenwu Wang
2010 2010 IEEE International Conference on Acoustics, Speech and Signal Processing  
First, the mixed signals are segmented to a number of blocks. For each block, the unknown mixing matrix is estimated in the transform domain by a clustering algorithm.  ...  The block-based operation has the advantage in improving considerably the computational efficiency of the compressed sensing algorithm without degrading its separation performance.  ...  An effective method for this problem is to use the socalled sparse signal representation, assuming that the sources are sparse or can be decomposed into the combination of sparse components [1] [2]  ... 
doi:10.1109/icassp.2010.5494935 dblp:conf/icassp/XuW10 fatcat:u4dfevwfefau5a5pimqiurptbm

Efficient Sparse Blind Source Separation Algorithm for two- Channel Acoustic Noise Reduction

Rédha Bendoumia
2019 Algerian Journal of Renewable Energy and Sustainable Development  
The TS-NLMS algorithm is proposed exactly when the convoluted mixing system is characterized by sparse impulse responses.  ...  In this paper, we propose new BSS structure based on the two-channel sparse normalized least mean square algorithm (TS-NLMS).  ...  Proposed Two-Channel Sparse FNLMS Algorithm In proposed algorithm, the adaptive step-sizes are calculated from the last estimate of the filter coefficients in an efficient way that step-size is proportional  ... 
doi:10.46657/ajresd.2019.1.1.4 fatcat:6l4l2jgtbzbtnoqi5sa7akcs7q
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